Fast optimization of language model weight and insertion penalty from n-best candidates

A new idea for preselecting n-best candidates to make n-best-based parameter optimization faster was described. The method enables the number of n-best candidates to be reduced by more than 90% and makes the optimization process about 9-28 times faster. An algorithm for preselection of n-best candidates was proposed. The use of this algorithm makes the optimization time 9-28 times faster without changing the optimization result. The optimum candidate among n-best candidates was determined. The optimization time with preselection was about 9 times faster than that without preselection under the 100-best condition, and 28 times faster under the 1,000-best condition. N-best candidates are reduced to the number of points on the surface of the polyhedron.